1. getMenuItemsJson — UNCHANGED

{
  "nodes": [
    {
      "id": "ribeye-id",
      "itemId": "pg-item-ribeye",
      "itemModifierId": "pg-im-ribeye",

To adopt: copy the block below into your ~/.claude/CLAUDE.md.

## How to decide what to do

Judge every piece of work by whether it **should** be done — is it correct, is the current state wrong or inconsistent, does it serve the goal — and **never by ROI, cost, effort, or "is it worth it."** Do not label a known-wrong thing "low-value," "marginal," "an edge case," or "not worth it" to justify leaving it unfixed; reasoning by ROI is exactly what keeps work mediocre. ("The reference / competitor also gets it wrong" is a *gap* argument, not a correctness one — it never makes a wrong thing acceptable.)

The only valid reason to stop short of doing the right thing is that it **provably cannot** be done — a demonstrated limit of the model or tools, not an assumed or cost-based one. "Hard," "heavy," "expensive," or "a lot of work" is never a reason to stop; "proven impossible / blocked" is. When unsure which it is, find out — try it, measure it, prove it — before deciding, and never declare a limi

\documentclass[12pt]{article} \usepackage[utf8]{inputenc} \usepackage{amsmath} \usepackage{graphicx} \usepackage{hyperref}

\title{Photonic Universe Hypothesis (PUH) v6: A Theory of Everything} \author{Brian Martell} \date{August 10, 2025}

Comment out this line from your VMX file: sound.virtualDev = "hdaudio"

If you don't want to do that, change the audio format settings in Windows to "24 bit, 44100 Hz (Studio Quality)" or higher, which greatly improves (※ but does not completely fix) the issue.

See this for more info: https://kb.vmware.com/s/article/2012007

Read more about Common Crawl

A pattern for building personal knowledge bases using LLMs.

This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.

Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.